Effect of Earnings Management on Bankruptcy Predicting Model Evidence from Nigerian Banks

International Journal of Trend in Scientific Research and Development (IJTSRD)
Volume 4 Issue 2, February 2020 Available Online: www.ijtsrd.com e-ISSN: 2456 – 6470
@ IJTSRD | Unique Paper ID – IJTSRD30187 | Volume – 4 | Issue – 2 | January-February 2020 Page 888
Effect of Earnings Management on Bankruptcy
Predicting Model: Evidence from Nigerian Banks
Emma I. Okoye, Ebele G. Nwobi
Department of Accountancy, Nnamdi Azikiwe University, Awka, Nigeria
ABSTRACT
This study determined the effect of EarningsManagement onBankruptcyRisk
in Nigerian Deposit Money Banks. The specific objectives are to examine the
effect of: Debt Covenant, bank Size moderates effect of Earnings Management
Incentives and bank Age moderates the effect of Earnings Management
Incentives on Bankruptcy Risk of listed Deposit Money Banks on Nigerian
Stock Exchange. The study employed Ex Post Facto research design and data
were collected via anuual reports and accounts of the sampled banks in
Nigeria. The formulatedhypotheseswereanalyzedandtestedwithRegression
analysis with the aid of E-view version 10 (2019). The result shows that debt
covenant has inverse significant effect on bankruptcy risk of listed DMBs in
Nigeria, implying that degree of debt covenant violations does not strongly
influences bankruptcy risk among Nigeria deposit money banks.Alsothatfirm
size moderates the effect of earnings management incentives on bankruptcy
risk, meaning that the behaviours of the earnings management incentives on
bankruptcy risk among Nigerian DMBs significantly and largely depends on
the size of the company. Another finding revealed that firm age has no
significant moderating effect on the nexus between the selected earnings
management incentives and bankruptcy risk of listed DMBs in Nigeria. The
study thereby recommended among others that even though higher debt
contracting does not necessarily result to insolvency, management should
ensure proper balancing of debt and equity in order to ensure a trade-off
between risk and return to the shareholders.
Keywords: Earnings Management on Bankruptcy Risk, Debt Covenant, bank
Size and bank Age
How to cite this paper: Emma I. Okoye |
Ebele G. Nwobi "Effect of Earnings
Management on Bankruptcy Predicting
Model: Evidence from Nigerian Banks"
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1. INRODUCTION
The issues of corporate financial distress and outright
business failure have been a great problem all over the
corporate world. Most high-profilecompaniesfromdifferent
regions (such as Enron and WorldCom) have collapsed in
recent past (in early year 2000’s) for several reasons –
ranging from unethical earnings distortions, fraudulent
accounting malpractices, and persistent poor overall
performance, among others. Apart from the highly
publicized cases of some notable foreign companies in year
2000’s, similar trends were equally witnessed in Nigeria
owing to the collapse of some financial (majorly deposit
money banks) and non-financial companiessuchasAfribank
Plc, Bank PHB, Oceanic Bank Plc, Intercontinental Bank Plc,
African Petroleum Plc, Lever Brothers and Cadbury Plc. An
investigation into the causation factors revealedsomedeep-
rooted problems in accounts preparation and calculated
misconducts of the managers and Chief Executive Officers
(Dibia & Onwuchekwa, 2014).
Reports from different researchers contest that despite
receiving clean bills of health from auditors, majority of the
distressed firms went bankruptnotlongafterdeclaringhuge
earnings in prior years (Dabor & Dabor, 2015; Dibra, 2016).
These usually lead to economic losses and social costs for
managers, investors, creditors, employees,government,and
so on. Thus, the recent upsurge in the research attention
towards the risk of bankruptcyanditspossibledeterminants
could be linked to the rekindled stakeholders’ quest for pre-
detecting and/or avoiding potentially-bankrupt firms in
order to minimizeinvestmentrisks(Hassanpour&Ardakani,
2017). In most developing countries, such as Nigeria, issues
regarding financial distressandbusinessfailureappearto be
more domiciled in the banking sector – owing to thenumber
of failed banks vis-à-vis the companies in other sectors
(Wurim, 2016).
Most of these cases of bank failures have been attributed to
high earnings management practices,poormanagementand
weak corporate governance structures (Akingunola,
Olusegun & Adedipe, 2013). Thus, in line with agency
relationship,whichseparatesthecompany’sownershipfrom
its control, the opportunistic behaviour of managers in
pursuing their own interest rather than those of the
shareholders’ can increasetherisk ofbankruptcy.Viacheslav
(2014) also stressed that aggressive earnings management
deficiencies and inappropriate allocation of resources are
two key factors capable of weakening the going-concern
status of the firm, which increases the threat of bankruptcy.
Thus, earnings management may trigger the risk of
bankruptcy by increasing uncertainty about future cash
flows and information asymmetry; andbyreducingfinancial
reporting quality (Biddle, Ma & Song, 2010).
This earnings managementoccursbecausetheapplicationof
accounting standards requires professional judgments so
sometimes managers use this discretion to create an image
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International Journal of Trend in Scientific Research and Development (IJTSRD) @ www.ijtsrd.com eISSN: 2456-6470
of the company that lack economic reality. Due to the
opportunities and loopholes offered in accounting policies,
managers with their background in businesschoosethe best
reporting methods, estimate and disclosures that match the
firm’s business economics.Earningsmanagementisakindof
artificial manipulation of earnings by management in order
to reach the expected level of profits for some specific
decisions (Degeorge, Patel and Zeckhauser, 1999)ascitedin
(Karami, Shahabinia & Gholami, 2017). A review ofEarnings
management practices before bankruptcy by Dutzi and
Rausch (2016) showed that there are different measurable
incentives for engaging in (either upwards or downwards)
earnings management. For example, (i) in periods of
wavering performance, stressed firms may engage in
‘income smoothing’ in order to meet or beat analysts’
earnings expectations (forecast) and keep the company’s
stock price up or steady. For opportunistic purposes, (ii)
managers may also engage in upwards earnings
management due to bonuses and compensations asbothare
based on earnings performance or can be tied to stock
options that generate profitwhenstock priceincreases.Also,
(iii) for tax planning purposes, firms seeking government
subsidy, incentives and exemptions maymanageearningsto
appear less profitable or engage in accrual manipulation by
minimizing reported earnings in order to reduce tax
payments (Biddle, Ma & Song, 2010). Similarly, (iv) firms
with higher debt ratios and low cash generation ability are
more likely to engage in extensive accrual earnings
management so as to ‘delay’ the violation of their leverage
levels stated in debt covenants or to suppress insolvency
signs (Dutzi & Rausch, 2016; Shabani & Sofian, 2018).
However, these different (measureable) incentives for
earnings management (that is income smoothing, executive
compensation/bonuses, tax planning, and debt covenant)
determine the probability of bankruptcy (bankruptcy risk)
appear not to have received immense research attention.
Relatedly, some bank-specific characteristics such as size
and age have been recurrently been pinpointed in literature
(see Rianti and Yadiati, 2018; Situm, 2014; Succurro &
Mannarino, 2011) as major “non-financial” determinants of
firms failure or success (Situm, 2014). Specifically,
researchers like Akkaya and Uzar (2011) claim that large
and older firms are more diversified, therefore theyfaceless
possibility of default than younger-smaller firms. This could
be observed by the proportion of failed new generation
banks vis-à-vis the older generation banks.
The problem of bank distress has become a great concern to
the stakeholders and the entire economy. This is because
when it is witnessed, the stakeholders especially the
shareholders lose the money they invested on the bank
shares and the economy will lack stability and growth.
Deposit Money Banks in Nigeria have witnessed several
recapitalization regulations from theCentral Bank ofNigeria
(CBN) in order to strengthen them. The last recapitalization
of N25 billion was meant to prevent Deposit Money Banks
from failing or going distress but the reverse was the case as
we currently witnessed the collapse of Sky Bank in the
middle of 2018 and Diamond Bank early 2019. Thisisa clear
indication that increasing minimum capital requirement of
banks alone only accounts for a short-term improvement in
the liquidity position of banks and improvement in their
asset quality but does not have the long term effect of
forestalling distress (Yauri, Musa & Kaoje 2012). Even the
present CBN governor, Godwin EmefieleinJune2019,stated
his plan for another bank recapitalization. This ignited this
research to find out other determinants of bank
distress/bankruptcy from the perspective of earnings
management practices.
Concerning the earlier identified earnings management
incentives (income smoothing, executive
compensation/bonuses, tax planning, and debt covenant),
researchers (for example, Healy and Wahlen, 1999; Jiang,
Petroni and Wang, 2010) have argued that engaging in
earnings management, either for management self-interest
(in line with agency theory) or for avoiding in-default
situations and regulatory costs (in line with signaling
theory), creates artificial volatility on share pricemovement
which reduces earnings quality and its decision usefulness,
and on the long-run may affect the performance and going-
concern status of the firm. Thus, it is most likely thatthefour
identified incentives for earnings management will most
likely explain the variations in bankruptcy risk. Soalsoisthe
assumption that the bank size and age could moderate the
extent to which the proposed earnings managementproxies
influence the level of bankruptcy risk.
However, these assumptions appear not to have been
empirically validated or rebuffed - save for Shabani and
Sofian (2018) who found evidence of a negative significant
effect of earnings smoothing and bankruptcyrisk butdid not
examine the otherincentivesnorincorporateanyinteraction
variable. Hence, the uniqueness of this study is its focus on
the identified earnings management incentivesinrelation to
bankruptcy risk, as well as consideringthemoderatingeffect
of bank size and age on the nexus between the independent
variables and the dependent (bankruptcy risk).
The broad objective of this study is to determinethe effect of
Earnings Management on Bankruptcy Risk among Deposit
Money Banks listed on the Nigerian Stock Exchange (NSE).
The specific objectives are:
1. To examine the effect of Debt Covenant on Bankruptcy
Risk of listed Deposit Money Banks on Nigerian Stock
Exchange.
2. To determine if Bank Size moderates the effect of
Earnings Management Incentives on BankruptcyRisk of
listed Deposit Money BanksonNigerianStock Exchange.
3. To ascertain whether Bank Age moderates the effect of
Earnings Management Incentives on BankruptcyRisk of
listed Deposit Money BanksonNigerianStock Exchange.
2. REVIEW OF RELATED LITERATURE
2.1. CONCEPTUAL REVIEW
2.1.1. Earnings Management
Capital market requires companies to give their earnings
estimates and the managers in order to keep the
shareholders happy, do everything necessary in order to
make their figures look good (Lo 2008). Managers’ pay are
mostly dependent on the value of their companies stock and
just like the average person, the manager wanted to earn
more money, which in turn meant that company
management focused less on the long-term ability to
generate cash (Brett 2010). The main reason for basing
manager’s pay on the value of company’s stock is to make
the executives more inclined to do a better job, without
foreseeing the possibility that they might concentrate on
how to increase the stock price every quarter in order to

earn more. Based on that, company’s executives and
managers have learnt how to manipulate earnings
(otherwise known asearningsmanagement)sothattheycan
artificially provide their desired firm financial performance.
Earnings management may involve exploitingopportunities
to make accounting decisionsthatchangetheearningsfigure
reported on the financial statements. Accounting decisions
can in turn affect earnings because they can influence the
timing of transactions and the estimates used in financial
reporting. Earnings management therefore, is a strategy
used by the management of a company to deliberately
manipulate the company's earningssothatthefiguresmatch
a pre-determined target. This target can be motivated by a
preference for more stable earnings, in which case
management is said to be carrying out income smoothing.
Thus, rather than having years of exceptionally good or bad
earnings, companies will try to keep the figures relatively
stable by adding and removing cash from reserve accounts.
Indirectly, management intentionally tries to maintain the
reputation of the firm by showing that their firms are
performing well in the market. As a result management will
gain better compensation such as bonus, prestige, job
security, pension contributions, stock awards and future
promotions (Bukit and Iskandar, 2006; Demirkan and Platt,
2009) as cited in Selahudin, Zakaria, Sanusi &
Budsarstragoon, 2014). Managers manage earningsinorder
to avoid reporting the volatility of the earnings and losses.
Sometimes managers tend to get involved in earnings
management in order to hide unlawful transactions and
hence, face high litigation risk (Jiang et al., 2008; Rahman
and Ali, 2006) as cited in Selahudin et al. (2014). Moreira
and Pope (2007) suggested that companies which have bad
news show higher earnings management (manipulation)
than companies which have good news.
Earnings Management Techniques
According to previous researchers there are two techniques
commonly recognized to manipulate earnings: accrual
manipulation and real activity manipulation. Until the
beginning of the 21st century, all of thestudiesusedaccruals
manipulation as a proxy for earnings management (Ruiz,
2016).
Accruals-Based Earnings Management
Accrual means for instance, when a firm makes a sale on
credit, the sale is recognized as earnings regardless of
whether cash has been received or not. This leads to the
creation of a receivable which is cancelled when cash is
received in the future (McVay, 2006). Accounting practices
allow discretion for managers in the financial information
provided. Managers can exploit thisbyrecognizing revenues
before they are earned or delaying the recognition of
expenses which have been incurred, which results in
accruals. Accruals-basedearningsmanagementoccurswhen
managers intervene in the financial reporting process by
exercising discretion and judgment to change reported
earnings without any cash flow consequences (Kothari, Shu,
& Wysocki, 2012).
Accruals can be splitintodiscretionaryand nondiscretionary
accruals, discretionary accruals being related to adjustment
to cash flows carried out by managers, while non-
discretionary accruals are those accounting adjustments to
the companies’ cash flows stated by accounting standard
setters. Firms can be aggressive with their accounting
choices by bringing forward earnings from a future period,
through the acceleration of revenues or deceleration of
expenses, thereby increasing earnings in the currentperiod.
This creates what is called discretionary accruals in the
literature. Since accruals reverse over time, earnings will be
lowered automatically by the amount of earnings that was
brought forward in the previous period.
Alexander, Britton and Jorissen (2011) affirm that accruals
include a component of subjectivity that are not completely
identifiable or observable, and managerscantakeadvantage
of this situation to achieve a desired result. Accrual
manipulation is originated by the valuation of accounting
items not directly related to changes in cash flow. Examples
are the choice among thedifferentdepreciationmethods,the
valuation of inventories or the choice of the basis for asset
valuation (historical cost or fair value), andtheestimation of
provisions. Furthermore, it has been shown that firms that
manipulate accruals will have to bear the costofthereversal
in subsequent years (Allen, Larson & Sloan, 2013) and for
this reason it gives limited benefits (Teoh, Welch & Wong
1998) as cited in Ruiz (2016). This leads to other means of
earnings management by managers.
2.1.2. Bankruptcy Risk
Bankruptcy actually reduces the likelihood of assets
disposals because assets are not sold quickly once a
bankruptcy filing occurs. Upon filing for bankruptcy, cash
does often not leave the firm without the approval ofa judge.
Without pressure to pay debts, the firm can remain in
bankruptcy for months as it tries to decide on the best
course of action. Eminentfailureorbankruptcyofbusinesses
and its prediction is of great importance to various
stakeholders including investors, suppliers, creditors and
shareholders. A business could fail as a result of economic
reasons, where a firm’s revenue cannot cover its cost,
financial where the firm is unable to meet its current
obligations even though its asset is more than its total
liabilities or bankruptcy if a firm’s total liability exceeds its
total assets.
Whitaker (1999) found that firms become bankrupt as a
result of economic distressstemmingfroma fall inindustry’s
operating income and poor management, arising out of
incessant losses over a period of five years. Whitaker’s
explanations of business failure seem to agree with the
economic and financial reasons given above, but differ from
the fact that, the fall in operating income is asa resultofpoor
management. Altman (2006) assigned managerial
incompetence as the most pervasive reason for corporate
failures. This assertion seems to agree with the view of
Whitaker that management incompetence is a main reason
for company failure. In recent times many business failures
have been attributed to poor corporate governance.
Corporate governance according to the Organizations for
Economic Co-operation and Development (OECD, 2008) isa
set of dependable relationship between the directors,
owners and other stakeholders of an entity. Corporate
governance also underpins the structural arrangements put
in place to enable the entity achieve its objectives and be
able to monitor and measure performance. Poor corporate
governance results in inappropriate decisions, lack of
supervision and oversight responsibility over company
activities, poor internal controls and abuse of powerbyboth

the board of directors and management. Competition is
another facet that when not properly managed would result
in business failure.
The empirical approach to bankruptcy prediction has
recently gained further attention from financial institutions,
mainly due to the increasing availability of financial
information (Agarwal & Taffler, 2008; Bisogno, 2012). The
global financial crises also increased the concerns towards
corporate bankruptcies necessitating its prediction. Thus,
forecasting bankruptcy has attracted the attention of
financial economists as it can provide a signal regarding the
company financial condition. A range of techniques have
been developed to predict bankruptcy. Starting from the
seminal paper of Beaver (1966), as cited in Bellovary,
Giracomino and Akers (2007) that first proposes to use
financial ratios as failure predictors in a univariate context,
and from the followingpaper ofAltman(1968),thatsuggests
a multivariate approach based on discriminant analysis,
there have been many contributionstothisfield(Poddighe&
Madonna, 2006; Ravi Kumar & Ravi, 2007). The next sub-
section highlights the different bankruptcy prediction
models.
2.1.3. Bankruptcy Prediction Model
A few years later (that is in 1968), Altman based his work;
the Z-score model on the study of Beaver (1966) and
published the first multivariate study (that is, analysis of
more than one statistical outcomevariableata time).Altman
(1968) applied multiple discriminant analysis within a pair-
matched sample and revolutionized corporate bankruptcy
prediction. Altman (1968) extended the univariate analysis
of Beaver (1966) by using more financial ratios in his
analysis. The model was based on a statistical method called
multiple discriminant analysis (MDA). The objective of the
MDA technique is to “classify an observation into one of
several a priori groupings dependentupontheobservation’s
individual characteristics” (Altman, 1968) as cited in
Gerritsen (2015).
These variables/ratios are chosen on the basis of their
popularity in the literature and potential relevancy to the
study. The result was a model with five different financial
explanatory variables and a qualitative dependent variable
(that is, bankrupt within 1-2 years or non-bankrupt). These
five variables are not the most significant variables when
they are measured independently. This is because the
contribution of the entire variable profile is evaluated bythe
MDA function (Altman, 1968). The Z-score has linear
properties as it involve the objective weighing andsumming
up of five measures to arrive at a singlevaluewhichbecomes
the criteria for classification of firms into various apriori
groups (healthy and unhealthy).
Altman then further revised the Z-score model where the
market value of equity was changed to the book value of
equity and the model was applicable to private and non-
manufacturing firms. He also came up with different
coefficients for the ratio as shown below.
Z’ = 0.717 X1 + 0 .847 X2 + 3.107 X3 + 0 .420 X4 + 0.998 X5
Where;
X1 = Working Capital/Total Assets
X2 = Retained Earnings/Total Assets
X3 = Earnings Before Interest and Taxes/Total Assets
X4 = Book value of equity/Book value of total liabilities
X5 = Sales/Total Assets
Zone of Discrimination
A. If Z is less than 1.23 = Zone of distress
B. If Z is between 1.23 and 2.9 = Grey zone
C. If Z is greater than 2.9 = Zone of safety
In 1995, this was further revised to include emerging
markets where the model could be used by both
manufacturing and non-manufacturingcompaniesaswell as
public and private firms.Themodel haddifferentcoefficients
and cut off points as follows.
Z” = 6.56X1+ 3.26X2 + 6.72X3 + 1.05X4
Where;
X5 = Excluded
X1 = (current assets – current liabilities)/Total assets
X2 = Retained earnings/Total assets
X3 = Earnings before interest and tax /Total assets
X4 = Book value of Equity/Total liabilities
Zones of discrimination
A. If Z is less than 1.1 = Distress zone
B. If Z is Between 1.1 and 2.60 = Grey zone
C. If Z is above 2.60 = Safe zone
The main criticism on the Altman models are based on, (1)
the age of the original Altman (1968) model and (2) on the
research design of themodels.First,Altman’s(1968)original
model is more than forty years old. It is likely that the
accuracy rate of the bankruptcy prediction models change
over periods of time when the setting of the study differs
(Grice & Ingram, 2001) as cited in Gerritsen (2015). Second,
the original parameters were estimated withtheuseofsmall
and equal sample sizes (33 bankrupt and 33 non-bankrupt
firms) this assumptions of normality and group distribution
downgrades the representativeness of the sample.
Considering that the original Altman’s model of 1968 and
revised model of 1995 measures the financial health of both
private and publically-listed firms in the United States.
2.1.4. Debt Covenants and Bankruptcy Risk
Another motivation is debt covenants. The firm’s closeness
to violating its debt covenant provides its management with
an incentive to engage in earnings management. Debt
covenants are agreements between a company and a
creditor usually stating limits or thresholds for certain
financial ratios that the companymaynotbreach.Depending
on the debt contract, a covenant breach can allow the lender
to convert its debt to equity, demandfull payback oftheloan,
initiate bankruptcy measures or adjust the level of interest
payments. Covenants can also be non-financial and for
example include specific events, such as change in
ownership of the firm. For creditors, covenants are "safety
nets" that allow them to reassess their lending when a risk
situation has changed.
Mulford and Comiskey (2002) describe debt covenants as
stipulations included in debt agreements designed to
monitor corporate performance.Forinstance,a lendermight
instruct that a certain value for an accounting ratio be
maintained or impose limits to investing and financing
activities. If the borrower violates the debt covenant, the
lender might increase the interest rate, requiring additional

financial security, or calling for immediate repayment.
Usually, a long-term debt contract has covenants to protect
debt-holders. If firms violate debt covenants, they will face
higher costs. Therefore, debt covenants provide incentives
for earnings management either to reduce the
restrictiveness of accounting based constraints in debt
agreements or to avoid the costs of covenant violations
(Beneish, 2001).
More importantly, Healy and Wahlen (1999) argue the need
for differentiating managers’ different opposing or
reinforcing motives so as to identify the magnitude of each
motive. For instance, managers may be motivated to
overstate earnings as a result of beating or meeting the
analyst forecasts or because of compensation contracts as
above mentioned. BhaskarandKrishnan(2017)conducteda
comprehensive study on the associations between debt
covenant violations and auditor actions for financially
distressed and non-distressed firms. They found that firms
with debt covenant violations have greater likelihood of
receiving a going-concern opinion, and a greater likelihood
of experiencing an auditor resignation. They also foundthat,
after controlling for other financial information, the relation
between debt covenant violations and an increased
likelihood of a going-concern opinion is stronger for non-
distressed versus distressed firms. Arens and Loebbecke
(2000) claim that violations increase auditors’ business risk
(that is, the risk that the auditor will sufferharmbecause ofa
client relationship) as it represent a new source and
indication of firm financial difficulty. They also prompt
heightened scrutiny by capital providers in the rare event
that a firm subsequently defaults on its debt or declares
bankruptcy.
2.1.5. Bank Size and Bankruptcy Risk
Several studies (such as Robert and Kim, 2007; Jacoby, Li
and Liu, 2017; Mohammadi, Mohammadi, and Amini, 2016)
have pointed out the importance of identifying confounding
factors that could moderate the relationship of earnings
management and bankruptcy prediction. Amongthiskindof
variables, Jacoby, Li and Liu (2017) identified company size
and age and controlled for them in their study of financial
distress and earnings management. However, for the
purpose of this study, the variables of bank size and age are
hypothesized and adopted as moderating variables. This
sub-section, and the next, reviews and describes the two
moderating variables in relation to the dependent variable.
The size of a firm is considered a vital variable in most
organizational performance evaluations; it has appeared in
several studies of business failure prediction as statistically
significant variable. Right from the work of Ohlson (1980),
the size of the firm has been one important predictor of
bankruptcy, which was significant in several periods before
the event of bankruptcy.
The same conclusion was found within the studies of McKee
(2007) or Fitzpatrick and Ogden (2011), whereas the
definitions for the size of the firm differed across these
studies. According to Situm (2014), it is assumed that the
size of the company and the age of the company are highly
correlated with each other. The growth of the firm seems to
be proportional to the size of the company (Thornhill &
Amit, 2003). Figure 2.1 below presents two curves for the
relation of the size of the company to the probability of
business failure (bankruptcy risk) based on two different
hypotheses. HypothesisAshowsa U-shapedcurveindicating
that there exists an optimal size of the firm, where the
probability of financial distress is the lowest. Firms with
greater size than this “optimal size” are more endangered as
they are assumed to have an inflexible organizational
structure. They have difficultiesinmonitoringmanagersand
employees as well as not perfectly functioning
communication structure (Dyrberg 2004).
2.1.6. Bank Age and Bankruptcy Risk
On company age, the general assumption is that the higher
the age of the firm is the probability ofbankruptcydecreases
(Situm, 2014). The reason behind this theory is that young
firms have knowledge about the average profitability, but
they most likely do not know their own potential. After they
have learned about their potential profitability they can
expand, contract or exit (that is, liquidate), based on the
position of the distribution of profitability. This will depend
on the ability of the firm to use inventionsandinnovations at
the right time. The winners of this competition survive and
remain on the market. These firms are increasing their
productivity. They are also able to develop technological
advantages, which are forcing losers to exit the market.
Firms having passed this situation will most likely show a
low probability of bankruptcy (Jovanovic & MacDonald,
1984).
Within the study of Altman (1968), the age of the firm was a
relevant indicator within his Z-score model to distinguish
between failed and non-failed firms. His second ratio
“retained earnings/total assets”implicitlycontainstheageof
the firm. Young firms will have a probably low ratio due to
lack of time to build up cumulative profits. A low value
implies a higher chance for the relatedfirmtobeclassifiedas
bankrupt. The probability of bankruptcy is higher for firms
in earlier years, which is well described by the mentioned
ratio and it also follows the shown path of the curve within
Figure 2.2 (Altman, 1968) as cited in Gerritsen (2015).
2.2. Empirical Review
Numerous studies have empirically examined the effect of
earnings management on several organizational outcomes.
In this study, the focus is on its relation with bankruptcy
and/or financial distress as well as other related names
usually used in describing financiallyunstablefirms(suchas
default, liquidation, exit, insolvencyandsoon).Itispertinent
to note that while a handful of studies exist on earnings
management among Nigerian authors, studies linking it to
the probability of bankruptcy are scanty and were largely
carried out in foreign countries. This sub-section
systematically reviews the previous studies related to the
study topic from whence the gaps were identified. The
review encompasses both those by foreignauthorsandtheir
Nigerian counterparts:
Muranda (2006) conducted a research with the purpose of
investigatingtherelationshipbetweencorporategovernance
failures and financial distress in Zimbabwe’s bankingsector.
He used the case study method and discussed cases ofbanks
currently in financial distress. Data collection was through
desk research. The analysis was qualitative and used
Judgmental sampling in selecting the eight abridged case.
The finding of the research revealed that in all cases of
pronounced financial distress, either the chairman of the
board or the chief executive wields disproportionate power

in the board. Gunay and Ozkan (2007) conducted a research
with a purpose of proposing a new technique to prevent
future crises, with reference to the last banking crises in
Turkey. They used Artificial Neural Network (ANN) as an
inductive algorithm in discovering predictive knowledge
structures in financial data and used to explain previous
bank failures in the Turkish banking sector as a special case
of emerging financial markets. Their findings indicate that
ANN is proved to differentiate patterns or trendsinfinancial
data. Olaniyi (2007) evaluated the susceptibility of Nigerian
banks to failure with a view to discriminate between sound
and unhealthy banks as a guide to investment decisions
using First Bank and Trade Bank as case studies.
Multivariate analysis of Z-score was carried out on the
secondary data obtained from the two Banks annual reports
and accounts between1998-2003 and it was concluded that
the model can measure accurately potential failure of
unhealthy banks but inaccurately failure status of sound
banks. Chung, Tan and Holdsworth (2008) in their study
utilized multivariate discriminate analysis and artificial
neural network to createaninsolvencypredictive model that
could effectively predict any future failure of a finance
company value in New Zealand. The results indicate thatthe
financial ratio of failed companies differ significantly from
non-failed companies. Failed companies were also less
profitable and less liquid and had higher leverage ratios and
lower quality assets. Garcia, Garcia and Neophytou (2009)
using a large sample of British Firms comparison with non-
bankrupt companies found thatbankruptcompaniesinthe4
years of pre-bankruptcy manage their profits in an
increasing form. Their study showed that the profit
management is done using both ways of earnings
management and accounting earnings management as a
result, the actual activities and management accounting
income decreased reliability. Chen, Chen, and Huang (2010)
in their research on “An appraisal of financially distressed
companies’ earnings management evidence from listed
companies in China” examined the earnings management
behavior of financially distressed listed companies in China
for the period 2002-2006, used discretionary accruals to
serve as a proxy variable for earnings management,withthe
type of ultimate ownership and the type ofindustrytowhich
the company belongs, functioning as independent variables.
The empirical results show that the desire to avoid
continued special treatment (ST) status and therisk ofbeing
de-listed leads firms to adopt different earnings
management behavior before and after being designated as
an ST firm. Saeedi, Hamidian and Rabie(2011)examinedthe
impact of earningsmanagementthroughmanipulationofthe
actual activities of the future performance of listed
companies at the Tehran stock exchange. A total of 123
companies listed in the stock exchange over a period of 9
years and the actual benefits management standards
provided by Garcia et al. (2009) and the future operating
cash flows and future operating profit is used as a measure
of future performance. The results show that there is an
inverse significant relationship between the real
management measures profit with future performance.
Oforegbunam (2011) instudyingBenchmarkingincidenceof
Distress in the Nigerian banking industry applied the
Altman’s model in the prediction of distress in the Nigerian
banking industry. Three banks (Union bank, Bank PHB and
Intercontinental Bank) that were declared distress during
the period of the study were used as case studies. Fouryears
financial statistics prior to distress was used to compute the
most discriminating financial ratios that were substituted
into the Altman’s model. The result of the analysis shows
that Altman’s model significantlypredictedthedistressstate
of each of the bank at 0.001level. Andualem (2011)
conducted a research on the determinants of financial
distress of selected firms in beverage and metal industry of
Ethiopia. His study estimated determinants of financial
distress using panel data starting from 1999 to 2005. The
results show that profitability, firm age, liquidity and
efficiency have positive and significant influences to Debt
Service Coverage as a proxy of financial distress. Hejazi and
Mohammadi (2013) in a research attempted to predict
Earnings management through nerve system and decision
tree in Corporations of Accepted in Tehran Stock Exchange.
Research results showed that the nerve system anddecision
tree method is more accurate in predicting Earnings
management in comparison to linear methods and have
lower error level. Zeytinoglu and Akarim, (2013) studied 20
financial ratios to predict the financial failure of firm listed
on Istanbul Stock Exchange National – All share index and
developed the most reliable model by analyzing these ratios
statistically. It identified that there are 5, 3 and 4 important
financial ratios in the discrimination of successful and
unsuccessful firms in 2009, 2010 and 2011 respectively.
Thus, the discrimination function is formed by using these
variables: capital adequacy and networking capital/total
assets ratios are seemed to be significant in all the three
periods. According to formed model, classificationsuccesses
are determined as 88.7%, 90.4% and 92.2% in 2009, 2010
and 2011 years respectively.
Khaliq, Altarturi, Thaker, Harun, and Nahar (2014) in their
research on “Identifying Financial Distress Firms: A Case
Study of Malaysia’s Government Linked Companies (GLC)”
addressed the financial distress measurement among 30
GLC’s listed companies in Bursa Malaysia over the period of
five years (2008 - 2012). Results showed that there were
significant relationship between both variables and Z –
Scores that determine financial distressed oftheGLC.Amoa-
Gyarteng(2014)investigatingAnglogoldashanti,a firmlisted
in Ghana, identifies some early indicators signaling
bankruptcy and the manipulation of financial statement. He
relied on the Modified Altman model and the Beneish model
in analyzing the financial statements of the company for the
years 2010 to 2012. The Beneishmodel wasusedtomeasure
the possibility of financial statement fraud, while the
modified Altman model was used as a predictor of
bankruptcy. The study could not be established bankruptcy
threat and evidences reveal that the probability of earnings
manipulation in the firm was low. Ahmed Mohammed and
Adisa (2014) in their study of “Loan Loss Provision and
Earnings Management in Nigerian Deposit Money Banks”
explores the relationship between loan loss provision and
earnings management in Nigerian DMBs. The result
indicated that there was a positive relationship between the
provision for loan losses and earnings management in
Nigerian DMBs. Ahmadpour and Shahsavari(2014)examine
the link between earnings management and the quality of
earnings using bankrupt and non-bankrupt firms listed in
the Tehran Stock Exchange for the period 2007 to 2012. The
analyses involve the use of an estimating unbalanced panel
data technique for the 198 non-bankrupt firms and the 55
bankrupt firms using Altman's model. It was found that the
bankrupt firms were inclined to opportunistic earnings
management than the non-bankrupt who choose efficient

earnings management. Ukessays (2014) studied the
importance of financial ratiosinevaluationoffirm’sfinancial
position and performance. Ten financial ratios coveringfour
important financial attributes namely: liquidity, activityand
turnover, profitability and leverage ratios were examined
under a two-year prior to bankruptcy. MultipleDiscriminate
Analysis (MDA) was used as statistical technique with the
help of SPSS 17.0 version on a sample of twenty six (26)
Bankrupt and 26 non-bankrupt firm two year prior to
bankrupt with an asset range of N5million to N750million
from 1996-2010. All companies were registered with
Karachi Stock Exchange. The result showed that profit
Margin, debt to equity ratio and return on asset has a
significant contribution in prediction of corporate
bankruptcy. Gerritsen (2015) examined the “Accuracy Rate
of Bankruptcy Prediction Models for the Dutch Professional
Football Industry”, tested and compared theaccuracyrateof
three commonly used accounting-based bankruptcy
prediction models of Ohlson (1980), Zmijewski (1984), and
Altman (2000) on Dutch professional football clubs between
the seasons of 2009/2010 - 2013/2014. The sample size on
the Dutch professional football industry throughout the
different seasons fluctuates between 30 and 36 depending
on the available data in a particular season of the annual
report and season reports. Overall,Zmijewski’sprobitmodel
(1980) performed most accurate on the Dutch professional
football industry within the five seasons of investigation.
Agrawal, Chatterjee and Agrawal (2015) studied earnings
management and financial distress:evidencefromIndia.The
article makes an attempt to empirically examine the
relationship between financial distress and earnings
management with reference to selected Indian firms. The
sample consists of 150 financially distressed firms during
the post-recession period from 2009 to 2014, used
discretionary accruals (DA) as a proxy for earnings
management. Multiple regression analysis was used for the
purpose. Altman’s Z-score (Z-score) and distance-to-default
(DD) have been used as two alternative measures for
financial distress. The study foundsthatlessdistressedfirms
were engaged in higher earnings management. Bisogno and
De Luca (2015) examined the relationship betweenfinancial
distress and earnings management practices in a family-
owned economic context, such as Italy, focusing on non-
publicly listed small and medium sized entities (SMEs).
Analyzing five years prior to bankruptcy, documented that
private SMEs experience financial distress, as measured by
subsequent bankruptcy filings, manipulate their financial
statements to portray better financial performance.
Madumere and Wokeh (2015) in their study of “corporate
financial distress and organizational performance: causes,
effects and possible prevention”examinedthecauses,effects
and possible solution of the dependent and independent
variables. In the cause of the study, literature was reviewed,
four hypotheses proposed, tested and analyzed using
Cronbach alpha; and the individual results came out high
mean and high standard deviation (on thevariousvariables)
with a p value of 0.000 ˂ 0.05 coefϐicient. The ϐindings
revealed that lack of financial discipline, technological
failure, over investment of fixed assets and government
policies are major factors of corporate financial distress.
Gebreslassie and Nidu (2015) studied the determinants of
financial distress conditions of commercial banks in
Ethiopia: A Case studyofSelectedPrivateCommercial Banks.
The study first assessed the financial healthconditionsof the
selected private commercial banks using Altman Z-score
model (ZETA Analysis) and estimated determinants of
financial distress using panel data starting from 2002/2003
to 2011/2012 and six private commercial banks in Ethiopia
using panel data regression, the researcher analyzed bank
specific factors affecting firm’sfinancial distress.Inthestudy
ZETA score of the banks was used as the proxy for financial
distress. Finding of the study indicate that capital to loan
ratio, net interest income to total revenue ratio have
statistically significant positive influence on the financial
health of banks whereas the nonperforming loan ratio has
statically significant negative influence on the financial
health of the banks. Egbunike and Ibeanuka (2015) studied
“corporate bankruptcy predictions: Evidence from selected
banks in Nigeria” and examined corporate bankruptcy
threats among selected banks in Nigeria.Data werecollected
from the annual reports of banks 2007-2011 available in
2010-2011 facts book of Nigerian Stock Exchange. In
addition to descriptive statistics, t-test difference between
mean, analysis of variance (ANOVA) test and multi-
discriminant model were used in analyzing the collected
data. The study identified five financial ratios – Working
Capital/Total Assets, Retained Earnings/Total Asset,
EBIT/Total Asset, Equity/Total Asset, Gross Earning/Total
Asset that could predict financial distress. Mohammadi,
Mohammadi, and Amini, (2016) carried out investigationon
the relationship between financial distress and earnings
management in corporations of accepted in Tehran Stock
Exchange during time between years of 2008-2015.
Research results showed that, in research hypothesis with
the increase of the free cash flow as a standard of financial
distress, earnings management will be increased. Kihooto,
Omagwa, Wachira and Ronald (2016) in their study of
“financial distress in commercial and services companies
listed at Nairobi Securities Exchange, Kenya” sought to
assess financial distress amongst commercial and services
companies listed at the Nairobi Securities Exchange Kenya,
with an objective of determining whether the companies in
this sector were prone to bankruptcy. The study utilized
secondary data collected from the Nairobi Securities
Exchange over a five year period (year 2009 to year 2013).
Using Altman’s Z score model, the study findings indicate
that the companies’ Z scores (on average) lay between, 1.88
to 3.5. This is an indication that the companies were
relatively not in danger of bankruptcy. Veganzones and
Séverin (2017) in their research on “the impact of earnings
management on bankruptcy prediction models” examined
the impact of two types of earnings management, accruals
and real activities manipulation, on bankruptcy prediction
models. The study provided evidences that when potential
accruals manipulation was measured, it allowed an
improvement of bankrupt firms, while real activities
measures, especially sales manipulation, enhanced the
prediction improvement of non-bankrupt firms. Their
finding suggests that failed firms were more likely to engage
on accruals manipulation, while healthy firms do it on real
activities manipulation. Nwidobie (2017) in studying
“Altman’s Z-Score Discriminant Analysis and Bankruptcy
Assessment of Banks in Nigeria” aimed to determine the
distress level subsisting in the bridge banks set up by the
Central Bank of Nigeria in 2011 to take over thenationalized
banks; and the 2011-classified unsound banks using the
Altman’s discriminant analysis model. Secondary data from
four sampled classified distressed and unsound banks from
the declared six for twoyearsprecedingtheirnationalization
and two years after using the stratified sampling technique

were analyzed using the Altman Z-score discriminant
analysis. Results showed that there were marginal
improvements in the financial status of the sampled banks
between 2010 - 2013 but they were still in a bankrupt
position with Union Bank Plc, Wema Bank Plc, Keystone
Bank Ltd and Mainstreet Bank Ltd having a Z-score of -0.56,
0.417, 1.5 and 0.45 respectively at 2013, all below the
minimum threshold of 2.675 for classification of a bank as
sound and non-bankrupt.ZhonglingandXiao(2017)provide
evidence that managersdownwardlymanageearningswhen
ex-ante distress risk non-trivially increases. As distress risk
increases, managers are under more monitoring by debt
holders seeking surer payment. Therefore, managerstend to
reverse previously accumulated positive earnings
manipulation and shift to a harder-to-uncover real activities
earnings management. Overall, they provided stronger
evidence on the subject as well as clarified previous
explanations through a larger sample and more selection
bias robust design.
Arzu, Gloria and Mantovani (2017) studied Forecasting
Bankruptcy: a European Analysis and analyzed the abilityof
Integrated Rating model to anticipate potential corporate
crisis. They studied bankrupt companies of four European
Countries (Czech Republic, Spain, Italy,France,Slovakia,and
the United Kingdom). They found that Integrated Rating
model can forecast bankruptcy(assessinga negativemeritof
credit judgment) with an accuracy that exceeds 75%.
Moreover, they tested and determined that the merit of
credit assessment is stable over time, and that, despite this,
the banking system does not recognize when a company is
not funded efficiently. Gusarova and Shevtsov (2017)
studied the Association between Accounting Manipulations
and Bankruptcy, likelihood analysis of 18 public companies
from the United Kingdom. The results showed that there
were no relations between the two phenomena. Therewas a
negative correlation found between the likelihood of
bankruptcy and the standard deviation. Since there was
almost no effect of the beta on the Altman Z-score, the
researchers concluded that the risk that causes the
probability of insolvency wasunsystematicandcomingfrom
the management of the company. Kanakriyah, Shanikat and
Freihat (2017) studied “Exploitation of Earnings
Management Concept to Influence the Quality of Accounting
Information: Evidence from Jordan” and aimed to
understand the nature impact of earnings management
practice on the quality of accounting information in Jordan.
The study used the quantitative technique to gatherthedata
by using a questionnaire to understand the users’ attitudes
about earnings management practice in Jordan and how the
practice influences the quality of accounting information.To
discover the issue, the researchers documented several
criteria to identify the conceptof"earning management''and
define the variables to measure it. The study states that all
accounting information users believe that the earnings
management practice was used by some companies in
Jordan. Erdogan and Adalessossi(2017)analyzedthefactors
that may have an impact on the banking sector, particularly
the ones which could be useful to predictthefinancial failure
of banks. The study compared and contrasted the banking
systems of Turkey and West African countries where the
main actor in financial system occurs to bethebanks.First,it
was found that (equity + profit)/total assets, fixed
assets/total assets, operating expenses/total assets,
(personnel expenses + severance pay)/total assets ratios
were effective in estimation of financial failure of banks in
Turkey. Second, Net Period profit/Average equity and
Interest income/Average earning assetsratioare effective in
prediction of financial failure of banks in the West African
Economic and Monetary Union. The study suggested that, to
determine financial failure of banks, various financial ratios
play a fairly consequential role for a variety of countries.
Shabani and Sofian (2018) studied “EarningsSmoothingand
Bankruptcy Risk in Liquidating Private Firms” using Altman
Z” score to measure firm’s specificbankruptcyrisk,thestudy
examined the association between accrual earnings
smoothing and bankruptcy risk in liquidating private firms
in UK and found that earnings smoothing significantly
negatively affects those firms' bankruptcy risk. The finding
implied that financially distressed firms engage with less
earnings smoothing, possibly because they do not have the
opportunity to engage in accrual earnings smoothing
anymore. Nonetheless, further examination showed that
those firms engage less with earnings smoothing because
they were being monitored by external creditors, indicated
by significantly high leverage during the last period before
they are being liquidated. Egbunike and Igbinovia (2018)
examined the impact of bankruptcy threatsonthelikelihood
of earnings manipulation in Nigerian listed banks. Using
Altman Z-score and Beneish M-score, the ex-post-facto
research design within a panel framework was employed
and binary regression models were used in testing the
hypothesis of the study via E-view 8.0. The study period was
2011 - 2015. The result revealed that bankruptcy threat has
no significant impact on the likelihood of an upward
earnings manipulation in Nigerian listed banks. The
implication is that manipulation of earnings in Nigerian
banks is spurred significantly by other factors outside the
threat of bankruptcy. By this, regulators and bank
managements are to place less emphasis on the bankruptcy
position of banks when probing into issues of earnings
manipulation because banks manipulate earnings not just
because of the threat of bankruptcy, as non-potentially
bankrupt firms also engage in upward earnings
manipulation. Hamid and Rohani (2018)studied“Predicting
financial distress: Applicability of O-score model for
Pakistani firms”. The study applied the most admired
financial distress predictionO-scoremodel andcomparedits
predictive accuracy with estimated logit model. The study
estimated logit model by including the profitability ratios,
liquidity ratios, leverage ratios, and cash flow ratios. The
study filled the gap by using the cash flow ratios to predict
financial distress for Pakistani listed firms. The sample for
the estimation model consisted of 290 firms with 45
distressed and 245 healthy firms for the period 2006 - 2016
and covered all sectors of Pakistan Stock Exchange. The
study provided important insights on the role of different
financial ratio in predicting financial distress and showed
that estimated logit model produces higher accuracy rate in
predicting financial distress. Farouk and Isa (2018) carried
out research on Earnings Management of Listed Deposit
Money Banks (DMBs) in Nigeria: A Test of Chang, Shen and
Fang (2008) Model. The study examined the effect of loan
loss provision on earnings management of listed DMBs in
Nigeria, using Chang, Shen and Fang (2008) model.Earnings
management variables comprises loan loss provision, total
assets, loan charge off and beginning balance of loan loss.
The population of 15 listed DMBs in Nigeria as at 2015.
Annual reports were used to obtain data and accounts of
banks which covers the period from 2008-2015. Panel
regression technique was adopted and Stata 13 was used as

tool of data analysis. The findings revealed that, all the
variables (loan loss provision, total assets, loan charge off
and beginning balance of loan loss) have significanteffect on
discretionary loan loss provision of the banks.
Below is the summary of the relevant literature reviewed in
this work towards appreciating the gap that prompted the
need for the research work.
It was also observed from the reviewed literature that none
of the previous related studies from Nigerian extracts
considered the interaction effect of some firm-specific
characteristics (such as size and age) in the relationship
between earnings management and bankruptcy risk.
Considering the size of the bank and its age can determine
both the level of earnings management as well as the
likelihood of bankruptcy, it is considered important to
explore their roles on how theearningsmanagementdrivers
triggers bankruptcy risk. To the best of the researcher’s
knowledge, none of the previous studies have taken the
above approach on studies related to earnings management
and bankruptcy risk. The closest is Jacoby, Li and Liu (2017)
who employed ‘political-affiliation’ as a moderator variable
in examining the relation between corporate financial
distress and earnings managementinChina andfoundthatit
weakens both associations. This study attempts to equally
contribute to existing knowledge from the above stated
dimension.
3. METHODOLOGY
3.1. Research Design
The study adopts ex-post facto (causal-comparative)
research design. This is a research design that seeks to find
relationshipsbetweenindependentanddependentvariables
after an action or event has already occurred. The
researcher's goal is to determine whether the independent
variables (that is earnings management) affected the
dependent variable (bankruptcy risks) by comparingtwo or
more groups of individuals using annual reports that
recorded events that have already occurred. It is used in the
study because of its suitability in the following ways: First,
the study is quasi-experimental studywhichexamineshowa
specified independent variable(s), affect(s) a dependent
variable.
3.2. Population of the Study
The study consist of the entire fourteen (14) deposit money
banks listed on the Nigerian Stock Exchange (NSE) between
years 2006 – 2018 (13 years). Thus, the period of thirteen
(13) financial years has been considered in order to capture
a long-enough bankruptcy trends among the listed banks in
the N25billion post-recapitalization era.
3.3. Sources of Data Collection
The study made use of secondary data obtained from
published annual financial statements of the listed banksfor
a period of 13 financial years 2006 to 2018. Specifically, all
the financial data were obtained from the library of the
Nigerian Stock Exchange (NSE) as well as from
www.nse.com.ng which is the official websiteoftheNSEthat
contains the database of all company’s annual reports in
Nigeria.
3.4. Model Specification
The empirical analyses involve three (3) panel regression
models in order to incorporate the two moderator variables
in separate panel regressions, and also address the
relationship between the selected earnings management
incentives on bankruptcy risk in separate panel regression
estimation. The dependent variable (bankruptcy risk) was
proxied using the modified Altman Z-scoremodel (2006)for
predicting bankruptcy. The original Altman (1968)measure
employed 5-weighted financial ratios and it only gauges the
financial health of publically-listed firms in the US. Altman
(1983, 1993) made several adjustments to the proxy
including only four (4) factors so that it can be applied to
firms in other contexts, while Altman (2006) further
modified the coefficients of these four factors to adopt the
model for emerging and developing markets called “The
Emerging-Markets Score Model (EMS Model) which this
study adopted.
Thus, the Altman’s (2006) EMS model yields a bankruptcy
measure (EMS score) that is a more appropriatemeasure for
both manufacturing and financial publicly-held
organizations in emerging economies and has beentested in
over 20 countries with high accuracy and reliability in
predicting bankruptcy (Li et al, 2011). The Z(EMS) score
model is given as follows:
Z(EM)" = 6.56*X1+ 3.26*X2 + 6.72*X3 + 1.05*X4 +
3.25…………………..(1)**
Where:
X1= Working Capital/Total Assets,
X2= Retained Earnings/Total Assets,
X3= Operating Income/Total Assets, and
X4= Book Value of Equity/Total Liabilities.
**Firms with a higher Z(EM) score are perceived to be more
financially healthy.
For ease of interpretation, a Z(EM) score below 1.1 indicates
a bankrupt condition. The assumption is that the dependent
variable, Z-score bankruptcy predictor, is a linear function of
the independent variable.
Z1 = f (Earnings management)…………………………………….. (2)
Where;
Z1 is the Bankruptcy risk (proxiedusingthemodifiedAltman
Z-scoremodel of2006);Earningsmanagement(independent
variable) will be classified into the four identified drivers
including: income smoothing, managerial incentives
(executive compensation), tax planning and debt covenant.
Accordingly, in line with the previousstudiesonbankruptcy,
the study re-modifies the empirical model of Jacoby, Li&Liu
(2017), and Veganzones and Severin (2016). The former
tested the moderator effect of political affiliation on the
relationship between financial distress and earnings
management while controlling for size and age. The latter
tested the impact of types of earnings management on
bankruptcy prediction.
The adapted models are specified below:
Model One:
Z-SCORE= β0 + β1INS + β2EC + β3TP + β4DC +
ε…………………....(Equ 4)
For the possible moderator effect of ‘bank size and age, the
study creates the following two (2) additional models inline
with the hierarchical moderation regression techniques:

Model Two:
Z-SCORE= β0 + β1DC + β2SIZ*INS*EC*TP*DC + ε………….(Equ
5)
Model Three:
Z-SCORE= β0 + β1INS + β2EC + β3TP + β4DC +
β5AGE*INS*EC*TP*DC + ε………..(Equ 6)
Where:
β0 = represents the constant
β1, β2 , ………and β5= represents the parameters to be
estimated
ε = represents the error term.
Z-SCORE = representsBankruptcyRisk (dependentvariable)
for the thirteen year period
DC=represents the debt covenant for the thirteen year
period
SIZ =represents the bank size for the thirteen year period
AGE =represents the bank age for the thirteen year
period
Our apriori expectations are as follows:
Β1>0, and β2> 0,
Where:
Β1>0: implies that the study expects that increase in the debt
covenant will likely lead to increases in bankruptcy risk.
Β2>0: implies that the study expects that bank size and age
will moderate the joint relationship between the identified
earnings management proxies and bankruptcy risk.
3.5. Method of data Analysis
For the purpose of the empirical analyses, the study used
both descriptive and inferential statistical techniques. The
analyses were performed using EViews 10 econometric
computer software. The descriptive analysis was conducted
to obtain the sample characteristics and to observe the
average bankruptcy level of the sampled banks. The panel
multiple regression analysis was performed to test how the
independent variables affect bankruptcy risk (proxiedusing
Altman Z-score, 2006); while the Hausman test was
conducted in model one to help chose between fixed and
random effect estimation techniques, the Hierarchical
Moderated Regression was deployed for models 2 and3due
to the inclusion of the moderating variables in both models.
Some conventional diagnostictestssuchasPanel Model Test,
normality, multicollinearity, heteroskedasticity,
autocorrelation and model specification test were equally
conducted to address some basic underlying regression
analysis assumptions.
4. DATA PRESENTATION AND ANALYSES
The analyses involved the application of descriptive
statistics, correlation matrix, panel data regression (for
model one) and hierarchical moderated regressions (for
models two and three). Model one represented the equation
without the moderators; whilemodel twoandthreehave the
moderating variables of size and age respectively. The
outcome of the panel regression estimations were used to
test the research hypotheses.
The entire results are presented in the following sub-
sections:
4.1. Univariate Analysis
Table2: Descriptive Statistics
Z_SCORE DC SIZ AGE
Mean 2.429852 0.949047 1571100225 30.07143
Median 2.069415 0.866380 1028005500 27.00000
Maximum 16.84797 7.992495 8223984226 58.00000
Minimum -2.37914 0.142118 52153878 16.00000
Std. Dev. 2.492597 0.610658 1637219052 10.80313
Skewness 2.719756 8.952718 1.627810 1.047457
Kurtosis 13.82242 99.56659 5.096202 3.086711
Jarque-Bera 1112.574 73146.64 113.6979 33.33770
Probability 0.000000 0.000000 0.000000 0.000000
Sum 442.2330 172.7266 2.86E+11 5473.000
Sum Sq. Dev. 1124.560 67.49557 4.85E+20 21124.07
Observations 182 182 182 182
Source: Eviews 10 (2019)
As observed from Table 4.1, the variable of debt-to-equity ratio (DC) has mean value of 0.949 implying that a large proportion
of the sampled banks are highly leveraged. The average INS showed a negative value of -0.059 indicating that majority of the
sampled banks engaged more in income-decreasing accrual management during the period covered by the study. The mean
value of variable of TP stood at 0.1526 indicating that, on average, the sampled banksarehighlytaxaggressivewith anaverage
effective tax rate of 15.3%.
On the firm size (SIZ) of the banks, run using the total asset values, the result showed that the joint average total assets of the
DMBs is ₦ 1,571,100,225 billion while the minimumandmaximumvaluesstoodat₦52,153,878and₦8,223,984,226billionfor
the 13-year period covered. The variable of AGE gave a mean value of 30.07 implying that the average year of incorporationof
the sampled banks is 30 years. The oldest bank among the log has been incorporated since 58 years ago while the youngest
bank is just 16 years old, based on year of incorporation. It is also observable from the probability values of the Jargue Bera
statistic that all the series are significantly lower than the 5% level, indicatingdeparturefromnormality.Thiscanbe attributed
to the usage of some of the variables in thier raw form (e.g. total assets, executive compensation and age) for the descriptive
statistics; they were then transformed into their log forms prior to regression estimations.

4.2. Test of Hypotheses
In order to answer the research questions, the six (6) null hypotheses earlier formulated in the chapter one of this study were
tested in this sub-section. The result of model one was used in testing Hypotheses One (Ho1), Two (Ho2), Three (Ho3) The
probability (sig.) values obtained from the regression result were used for the hypotheses tests.
Decision Rule:
The decision rule goes thus: the null hypothesis will be accepted if the probability value (p-value) is greater than 0.05 orwhen
the calculated t-statistics is less than 2.0, or reversely we reject the null hypothesisiftheprobability(p-value)valueislessthan
0.05 and or the t-statistics is ≥ 2. The summary of the hypotheses results are shown in Table 4.8 below:
Table3: Summary of Hypotheses Testing
Hypotheses Prediction Actual Result Decision
Ho1 Debt covenant does not affect bankruptcy risk
Significantly
positive
Negative – Insignificant
(p-value=0.66)
Accept null
Ho2
Bank size does not moderate earnings
management incentives’ effect on bankruptcy risk.
Significantly
negative
Negative – Significant
(p-value=0.048)
Reject null**
Ho3
Bank age does not moderate earnings management
incentives’ effect on bankruptcy risk.
Significantly
negative
Positive – Insignificant
(p-value=0.903)
Accept null
Source: Researcher’s compilation (2019) **.Statistically significant
Hypothesis One: Debt Covenant has no significant effect on
Bankruptcy Risk of listed Deposit Money Banks on NSE.
The result of this hypothesis in Model 1, Table 4.7 showed
that the variable of debt covenant has negative coefficient
sign and an insignificant probability value (-0.098035,
p=0.6627>0.05). This means that Debt Covenant is not
statistically significant on bankruptcyrisk.Basedonthat,the
study accepted the null hypothesis. The implication is that,
increases in debt ratio (DC) have the tendency of causing a
decreasing effect on bankruptcy risk, although not
significantly.
Hypothesis Two: The effect of Earnings Management
incentives on Bankruptcy Risk among listed Deposit Money
Banks on NSE is not moderated by the bank size.
Our result of the hypothesis in Model 2, Table 4.7 regarding
the moderating effect of firm size showed thatfirmsizehasa
significant negative moderating effect on the relationship
between the selected earnings management incentives and
bankruptcy risk (-0.290418, p=0.0484<05). The study
rejected the null hypothesis and accepted the alternative,
that bank size moderates the effect of earnings management
on bankruptcy risk. The implication oftheinteractionoffirm
size is that there is tendency that income smoothing,
executive compensation, tax planning and debt covenant
effects on bankruptcy risk are significantly reduced as the
size of the firm increases.
Hypothesis Three: Bank Age does not moderate the effect
of Earnings Management incentives on Bankruptcy Risk of
listed Deposit Money Banks on NSE.
The result of the test in Model 3, Table 4.7 showed that firm
age has no significant moderating effect on the earnings
management (0.002059, p=0.9027>0.05). This shows that
there is positive insignificant relationship between bank age
moderator and earnings management. The study accepted
the null hypothesis. The resultimpliesthatthejoint effectsof
the explanatoryvariables,takentogether,onbankruptcy risk
cannot be altered irrespective of the age of the firm.
4.3. Discussion of Findings
From the first hypothesis, the results showed that the
variable of debt covenant has negative coefficient sign and
an insignificant probability value which lead to the
acceptance of the null hypothesis (Ho1). What this portends
is that firms with stiffer debt covenants or higher debt ratio
have lower probability of bankruptcy risk. This did not
conform to our apriori expectation that highly indebted
firms are more likely to have higher bankruptcy risk. Our
expectations conforms with the position ofNwude,Agbadua
and Udeh (2016) that firms nearingdebtcovenantviolations
have greater likelihood of having a going-concern
uncertainty, which represents an indication of firm financial
difficulty. However, our result finds support with many
empirical researches implying that debt violations seldom
lead to liquidation or bankruptcy(thatis,DichevandSkinner
2002); instead, such violations commonly result in
renegotiation or waiver by the lender (Nini, Smith & Sufi
2012). Desai, Foley and Hines (2003) also found that firms
with high debt ratios enjoy debt tax shield benefit and get
reduced corporate tax waivers. Thus, since high corporate
tax rates may lead to greater corporate indebtedness which
affects the earnings, there is possibility that firms entangled
in high debt covenants may likely notexperienceinsolvency;
rather they become more disciplined tore-strategizeand act
more in the interests of shareholders (Berger & Di Patti,
2006). The recent study of Spyridopoulos (2018) which
found that stricter debt covenants have positive and
economically large effect on the borrowers' operating
performance - also corroborates our findings. Possible
reasons may include the contention that managers of low-
indebted firms are inclined to spend free cash flows more
freely, thus taking less effective projects and generating
lower return.
Thus, there are limited long-term debts in many developing
countries such as Nigeria owing to weak financial and legal
institutions; as a result, creditors often use short term debt
to monitor and discipline borrowers’ behaviour (Nwude et
al, 2016). Therefore, since firms that are heavily debt
financed offer creditors less protection in the event of
bankruptcy and harbour implicational costs on the
management and the firm (in terms of firm value and
reputation), the tendency for renegotiations and greater
financial discipline by management becomes improbable as
a way of reducing the risk of bankruptcy.
Our result on the second hypotheses regarding the
moderating effects of bank size showed that firm size has a
significant negative moderating effect on the relationship

between the selected earnings management incentives and
bankruptcy risks. This led to the rejection of null hypothesis
(Ho2). The implication of the outcome of the interaction of
firm size is that the tendency that income smoothing,
executive compensation, tax planning and debt covenant
affects bankruptcy risk are significantly reduced as the size
of the firm increases. The outcome aligns with our
expectation. The study expected, in line with Akkaya and
Uzar (2011), and Rose-Green and Lovata, (2013), thatlarger
firms are more likely to reorganize even after being in the
“alert zone” (that is, high risk of going bankrupt) and
subsequently escaping liquidation, thansmallerones.This is
because large and older firms have more access to debt
financing than smaller and newer firms and that may be a
source of recovery, even when the risk of bankruptcy
appears inevitable. However, our empirical evidence
supports our conjecture on firm size.
Even in practice, as regards the DMBs in Nigeria, since the
CBN’s bank recapitalisation of Nigerian banks in 2005 when
they were 89 operational banks in Nigeria, till currently
when they are just 14 operational deposit money banks,
among the factors that are common to the banks that have
survived all these turbulent years, is size and age. The
outcome of most previous studies also supports this
conjecture. Specifically, our result on firm size agrees with
Situm (2014), Theodossiou et al (1996), Chava and Jarrow
(2004) who also found that, firms’ probabilities of
bankruptcy are largely dependent on the size.
Our result on the third hypothesesregardingthemoderating
effects of firm age showed that the variable of firm age has
no significant moderator effect on earnings management.
This led to the acceptance of the null hypothesis (Ho3). The
result implies that the joint effects of the explanatory
variables, taken together, on bankruptcy risk cannot be
altered irrespective of the age of the firm. However, our
empirical evidence do not supports our conjecture on firm
age. However, the non-significant moderating effect of firm
age is at variance with Rianti and Yadiati (2018) and
Succurro & Mannarino (2011) which showedthatfirmageis
among the major ‘non-financial’ determinants offirmfailure
or success. It also negates the findings of Aleksanyan and
Huiban (2014) which concluded that the incidence of exit
(eventual bankruptcy) varies as a function of firm age.
5. SUMMARY, CONCLUSION AND RECOMMENDATIONS
This chapter presents the summary of this research study
and its major findings from where it drew its conclusions. It
also presents the implication of the findings, the major
contributions to knowledge and policy recommendations as
well as possible avenues for future researches.
5.1. Summary of Findings
Based on the outcome of the empirical analyses in the
previous chapter in relation to the specific research
objectives, the major findings of the study can be
summarized as follows:
A. That debt covenant has inverse significant effect on
bankruptcy risk of listed DMBs in Nigeria, implying that
degree of debt covenant violations does not strongly
influences bankruptcy risk among Nigeria deposit
money banks.
B. That firm size moderates the effect of earnings
management incentives on bankruptcy risk, meaning
that the behaviours of the earnings management
incentives on bankruptcy risk among Nigerian DMBs
significantly and largely depends on the size of the
company
C. That firm age has no significant moderating effectonthe
nexus between the selected earnings management
incentives and bankruptcyrisk oflistedDMBsinNigeria.
5.2. Conclusion
The study empirically examined the effect of earnings
management on bankruptcy risk of Nigerian listed DMBsfor
a period of thirteen (13) financial years. Four incentives for
earnings management were selected as independent
variables, against bankruptcy risk as dependent variable.
Two other firm-specific attributes (size and age) were also
drafted as moderating variables in an estimation covering
182 firm-year observations. The study employed the use of
descriptive statistics, correlation analysis, and panel
regression model for the estimations. The results showed
that while debt covenant did not assert any significant effect
on bankruptcy risk. Similarly, the interaction of firm size
significantly moderates the joint relationships between the
explanatory variables and the dependent variable of
bankruptcy risk that of firm age appeared statistically
insignificant when used in the same capacity.
Based on these outcomes, it canbeconcluded,therefore,that
in terms of the effects of earnings managementincentiveson
the bankruptcy risk of Nigerian listed deposit money banks,
while the variables of debt covenant and income smoothing
were insignificant and can be considered as not of crucial
importance in the context of bankruptcy risk determinants
among Nigerian banks.
5.3. Recommendations
Based on the findings, the study makes the following
recommendations:
A. Even though higher debt contracting does not
necessarily result to insolvency, management should
ensure proper balancing of debt and equity in order to
ensure a trade-off between risk and return to the
shareholders.
B. As firm size moderates the effects of earnings
management and it is determined by the total asset of a
firm, management is therefore encouraged to always
utilize firms’ assets properly by also injecting surplus
fund to assets so as to enable growth instead of
spending on non-value maximizing things like cars and
others.
C. As bank age has no significance effect on earnings
management, the study recommends that both the old
and newly incorporated banks management should
strictly desist from practicing earnings management so
as to avoid the disastrous end result, which is
bankruptcy.
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